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ISACA AAIR Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Topic 2: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 4: AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
ISACA Advanced in AI Risk Sample Questions:
1. Which of the following is the MOST appropriate key performance indicator (KPI) for the effectiveness of a targeted AI risk awareness training program?
A) AI solution adoption rates among business units whose employees have taken training
B) Percentage of users who can identify the financial impact of AI outcomes
C) Changes in risk ratings for risk scenarios involving awareness of insider threats
D) Number of AI irregularities or potential tampering incidents reported by users
2. An organization has deployed an AI system to automate critical data analysis functions. Which of the following is the MOST appropriate way for the risk practitioner to assess the multiple sources of risk associated with this situation?
A) Rate each risk factor independently as a basis for ordering mitigation actions.
B) Quantify the financial impact of competitors' realized risk events on AI initiatives.
C) Document the exploitable technical limitations of all AI system components.
D) Prioritize the risk factors most likely to generate substantial harm.
3. Which of the following is the MOST important consideration when managing changes to an AI model in production?
A) Allowing operational teams to adjust configuration parameters for real-time performance tuning
B) Expediting rollout of changes in production to ensure service continuity and minimize downtime
C) Implementing stringent approval processes for user access to new model functionalities
D) Conducting rigorous validation to assess effects on predictive accuracy and model bias
4. Which of the following AI system considerations BEST mitigates risk associated with model drift?
A) Restricting the use of automated data validation to low-risk models
B) Conducting regular retraining with new relevant datasets
C) Maintaining existing levels of variance within datasets during preprocessing
D) Implementing strong access controls based on roles and responsibilities
5. Which of the following BEST mitigates risk associated with evasion attacks on AI models?
A) API rate limiting
B) Predictive analytics
C) Feature importance weighting
D) Anomaly detection
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: D |







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